IBM Cloud TSL
AI is becoming more and more integrated into business models as organisations use it to improve consumer experiences and streamline operations. As a result, efficient infrastructure design, implementation, and maintenance are now required to ensure the seamless functioning of mission-critical AI-enhanced applications.
IBM TLS Services
A comprehensive variety of integrated data centre services and support are offered by IBM Technology Lifecycle Support (TLS) in an effort to hasten their clients’ transition to hybrid cloud and artificial intelligence. IBM TLS offers software and enterprise networking support services in addition to services for IBM infrastructure products and solutions from top third-party systems.
IBM can provide assistance when issues arise in addition to assisting businesses in proactively and preemptively avoiding them because to their AI-infused technological base. As a result, IBM is able to support their clients in maintaining and delivering high availability and resilience levels.
IBM’s quest to integrate AI with assistance
IBM is early adopters of AI and automation technology to handle the scale, complexity, and criticality of the infrastructure that IBM TLS provides. To provide cutting-edge support at scale and across a wide range of products, IBM consistently implement new technologies, from rule-based systems to sophisticated machine learning models to generative AI.
IBM collaborate with IBM Research and make use of the innovative infrastructure and software technologies from IBM. IBM leverages AI support as Client Zero for Watsonx use cases in order to enhance the customer and employee experience.
The IT support industry is ripe for innovation since it has so much opportunity to use AI in all of its forms. AI will be used more and more by the support staff of the future to provide value to engineers, clients, and the partner ecosystem.
IBM’s solutions are made so that customers can get more out of IBM and their partners‘ systems in terms of resilience, availability, and dependability. By doing this, they may benefit both their internal IT team and their clients.
Companies want results from their customer service, and IBM meet those expectations
IBM’s support engineers benefit from deeper, more proactive insights, tailored, and practical problem-solving solutions, as well as the automation of tedious jobs and processes, thanks to AI-based assistants. Nearly 1,500 customer service managers, directors, and executives from companies using conversational AI for at least a year, spanning 34 countries and all major industries, were polled by the IBM Institute for Business Value (IBV).
Nearly two-thirds of corporate leaders questioned expect generative AI to boost customer satisfaction, and over half anticipate stronger revenue growth, customer retention, and human agent satisfaction, according to the IBV report “Customer service and the generative AI advantage.”
IBM TLS offers their clients AI-enhanced solutions and services to help them accomplish their IT objectives and gain a deeper understanding of their surroundings, including proactive perspectives.
For instance, clients may gain greater insights about their assets, infrastructure security concerns, and case analytics with IBM Support Insights product. IBM’s extensive service offering covers every stage of the product lifecycle. It covers the design, development, deployment, maintenance, update, and decommissioning of new AI systems as well as core mission-critical systems. They demand comprehensive knowledge of computation, networking, storage, performance, and scalability.
By utilising artificial intelligence and automation tools, IBM can offer customer care via phone, email, and chat. This enhances the general customer experience by enabling their support personnel to provide more knowledgeable assistance.
TLS IBM
Applying a data-driven methodology with Watsonx to enhance support services
Utilising the IBM Watsonx suite of products for governance, AI, and data, TLS develops fresh, cutting-edge solutions to assist their clients in getting the most out of their infrastructure investment. IBM also obtain input from their implementation regarding the functionality of IBM software and infrastructure products, enabling operations for their many thousands of customers with perceptive, personalised, and proactive assistance.
IBM used a data-driven strategy to track how AI has improved their delivery and support procedures. In order to generate key performance indicators (KPIs) that power a continuous improvement process, IBM collect business and technical measurements. These Key Performance Indicators (KPIs) include the amount of issues that were proactively identified and resolved, the effectiveness of IBM’s self-service virtual assistants, and increases in their customers’ Net Promoter Scores (NPS). IBM also recorded measurements for the amount of time it took to address customer issues and associated process inefficiencies.
Overcoming obstacles to satisfy customers
IBM TLS provides their clients with many services. IBM offer partner products that use AI for self-service and delivery together with integrated product support for the data centre across all IBM infrastructure. Additionally, IBM offer AI-powered insights on cases, risks, and supported assets. IBM provide their clients with offerings and services that evaluate, implement, and decommission infrastructure in order to expedite the delivery of AI solutions to their stakeholders.
These are the main client objectives when it comes to product support, based on experience and knowledge with clients:
- Responsive customer service: Make rich insights, proactive automated notifications, and personalised self-service access available to help their clients’ SREs perform better.
- Superior assistance and services: Utilise knowledge from several situations combined with individualised context to help their support engineers optimise system uptime and provide better quality of service.
- Effective assistance and services: IBM continuously assess and enhance the effectiveness of their back-end operations to minimise bottlenecks and expedite responses.
To supply these capabilities, artificial intelligence (AI) and automation (in all their forms, including the newest generative models built on the IBM Watsonx platform) are essential. However there are a number of obstacles to putting AI into practice, including as
- Managing the complexity resulting from the variety of product versions, implementation-specific customisation, and integration, as well as the infrastructure.
- Gaining access to data sources while taking compliance, privacy, and multilingual issues into account.
- Taking into account the human factor when working with mission-critical systems that have a low tolerance for downtime and the potential to have significant effects on the economy and regulations.
To meet these problems, IBM TLS is presently collaborating closely with the software, research, and CIO teams within IBM. IBM is putting new, scalable methods for vectorising, sorting, and summarising extensive product documentation into practice. IBM’s objective is to give their engineers the tools they need to respond to issues based on comparable past cases and to help their clients via self-service channels by applying Retrieval Augmented Generation (RAG) techniques.
In order to promote continuous improvement, IBM is also putting in place client, engineer, and LLM-based feedback loops, as well as uniform testing frameworks and effectiveness and accuracy metrics for the underlying models. IBM chose a platform strategy that makes use of shared code from several projects as well as contributions and consumption from both open-source and internal sources.
IBM goal at IBM TLS is to help other client-facing teams and capitalise on IBM’s learnings to provide best practices and implementation insights to clients as they embark on AI journeys.
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